What the data for this category looks like
The data sources for this category are landing page access logs, heatmap data, form submission data, and ad source parameters. Data update frequency is weekly or within a single campaign cycle. Daily associated documents include landing page plans, buried point requirement lists, A/B test reports, and form field configuration tables. Data fields include page path, dwell time, form submission rate, and source channel identifier. Corresponding units are seconds, percentage, entries, and channel code. Daily analysis requires integrating multi-source data. Field formats in documents must be strictly matched to avoid mixing data across campaigns.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
The strong binding between this category’s data and documents requires multi-turn dialogue to strictly limit context scope. Only conversation content from the current campaign cycle may be retained, and redundant data from past campaigns must be excluded. The data update frequency of weekly or single campaign cycle means conversation context should not retain more than 12 rounds of historical content. Otherwise, analysis bias may occur due to expired data. The structured nature of daily documents requires prompts to clearly align with preset field formats, guiding the model to output content that meets form or buried point requirements. The need for multi-source data integration requires multi-turn dialogue to gradually break down analysis tasks, step-by-step guiding users to provide corresponding source data. Requesting too much information at once is avoided to prevent chaotic output. Each campaign’s dialogue session must also remain independent to prevent interference between session data of different campaigns.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the landing page campaign | Enable independent dialogue for each campaign, avoid session data mixing |
messages | Retain the most recent 8-12 rounds of conversation content | Adapt to the weekly update analysis rhythm, control context overhead |
variables | Pre-pass the current campaign's form fields and buried point parameter list | Align with daily document structures, reduce output format deviation |
| Session timeout period | 600 seconds | Adapt to the weekly analysis task cycle, avoid mid-task interruptions |
| Prompt template | Limit output format to preset document fields | Constrain model output to meet business requirements |
Three common configuration mistakes
- Shared session data across campaigns: Dialogue content from different landing page campaigns interferes with each other. The root cause is failure to correctly bind
chatIdto the campaign’s unique identifier. - Non-streaming model output: Model responses have no segmented loading effect, with all content returned at once. The root cause is incorrect configuration of context refresh rules, or failure to enable streaming transmission parameters.
- Format errors caused by prompts: The model’s output form fields have extra spaces, uppercase letters, or unnecessary modifications to link content. The root cause is failure to clearly align with preset document field formats in the prompt, and failure to pass
variablesto constrain output rules.
How to confirm the configuration is correct
- Launch a dialogue for the corresponding landing page campaign, check if
chatIdmatches the campaign identifier. The binding relationship can be viewed via FastGPT v4.17.0 session logs. - Input multi-turn analysis questions, observe if the context only retains the most recent 8-12 rounds of historical content, with no redundant data from past campaigns causing interference.
- Pass the preset form field list, verify that the model's output strictly matches the field format, with no extra spaces or case modifications.
- Send a test request, confirm that the model response enables streaming output, with content displayed in segmented loading.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.